Optimization of Wireless Positioning and Sensing Network (WPSN) Architecture using Machine Learning Technique

无线定位和传感技术正在成为利用磁或电磁响应交换来跟踪目标的最新技术策略。无线阅读器是这项技术的关键组成部分，它们配备了发送和接收信号的天线、收发机和解码数据的处理器。最近，Peytec Inc.设计和制造了用于无线定位和传感网络(WPSN)应用的智能传感器，使用可扩展的无线通信系统来感知和跟踪室外/室内物体位置数据，以创建到库存系统的自动交易。在室内工业环境中传感位置信息需要高精度的无线通信系统，这在一定程度上是一项具有挑战性的任务，因为各种物体反射和分散信号，这对传感和定位数据的通信可靠性产生了不利影响。目前，Peytec Inc.只能在没有障碍物的情况下(在10厘米的可接受精度范围内)收集信息，但是一旦引入障碍物，精度随着旅行时间的变化而急剧下降。该项目的目标是开发机器学习算法，以达到所需的精度定位对象，并针对现有的静态和动态障碍物构建最优的无线定位和传感网络(WPSN)，用于室内定位。该项目将汇集瑞尔森大学和Peytec Inc.的人才，并利用Ryerson的资源，包括经验丰富的研究人员、精力充沛、目标驱动的研究生，以及Peytec Inc.在WPSN设计和开发方面的卓越记录，通过利用历史数据、培训数据，特别是机器学习(ML)方法，共同探索跟踪产品所需的最佳读者数量。成功设计优化问题和ML算法以确定最佳WPSN架构将为加拿大安大略省Peytec公司带来新的差异化产品能力、市场拓展和销售。此外，该项目还将通过让HQP从事行业级产品的开发和设计，为HQP提供丰富的培训环境。

Wireless Positioning and Sensing technology is emerging as the latest technological strategy in tracking objects using magnetic or electromagnetic response exchange. Wireless readers that are key components of this technology are equipped with antennas for sending and receiving signals, a transceiver and a processor to decode data. Finding optimal number and placement of these readers for indoor and outdoor purposes are an important issue in these days.Recently, Peytec Inc, designed and fabricated smart sensors for Wireless Positioning and Sensing Network (WPSN) application using scalable wireless communication system to sense and track oudoor/indoor object location data to create automated transactions to the inventory system. Sensing location information in indoor industrial environments requires a high precision wireless communication system and is a challenging task in part because various objects reflect and disperse signals which adversely affects reliability of communication of sensing and location data. Currently, Peytec Inc. is only capable of collecting the information when no obstacle is present (within an acceptable accuracy range of 10 cm), however once an obstacle is introduced the accuracy drops dramatically as the travel time changes.The objective of this project is to develop machine learning algorithms to localize object with required precision, and to architect optimum Wireless Positioning and Sensing Network (WPSN) with respect to existing static and dynamic obstacles for indoor localization purposes. This project will pull together talents from Ryerson University and Peytec Inc. and take the advantage of the resources of Ryerson including experienced researchers, energetic and goal-driven graduate students with a track record of excellence of Peytec Inc. in design and development of WPSN to jointly explore the optimal number of required readers for tracking products by utilizing historical data, training data and in particular, Machine Learning (ML) approaches. Successful design of optimization problems and ML algorithms to identify optimum WPSN architectures will enable new differentiated product capabilities, market expansion and sales for Peytec Inc. Ontario, Canada. In addition, this project will provide a rich environment for the training of HQP by having them engaged in development and design of industry-caliber products.